| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1265 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 13.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1265 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "whisper" | | 1 | "glistening" | | 2 | "grave" | | 3 | "tension" | | 4 | "palpable" | | 5 | "flicked" | | 6 | "flickered" | | 7 | "echoed" | | 8 | "weight" | | 9 | "scanned" | | 10 | "reminder" | | 11 | "calculating" | | 12 | "tinged" | | 13 | "loomed" | | 14 | "determined" | | 15 | "unreadable" | | 16 | "trepidation" | | 17 | "almost alive" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "eyes narrowed" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 86 | | matches | (empty) | |
| 93.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 86 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1255 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1148 | | uniqueNames | 18 | | maxNameDensity | 0.87 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 10 | | Covent | 1 | | Garden | 1 | | River | 1 | | Thames | 1 | | Tomás | 1 | | Herrera | 10 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 2 | | Silas | 1 | | Tonight | 2 | | London | 2 | | Veil | 3 | | Market | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Silas" | | 7 | "Tonight" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Covent" | | 2 | "Garden" | | 3 | "River" | | 4 | "Thames" | | 5 | "Raven" | | 6 | "Nest" | | 7 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 3 | | matches | | 0 | "smokiness that seemed to cling to the walls" | | 1 | "seemed all too final" | | 2 | "seemed almost alive glowing brighter as it passed to her" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.797 | | wordCount | 1255 | | matches | | 0 | "not hers but ripe with danger" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 38.03 | | std | 19.86 | | cv | 0.522 | | sampleLengths | | 0 | 74 | | 1 | 55 | | 2 | 46 | | 3 | 62 | | 4 | 53 | | 5 | 47 | | 6 | 77 | | 7 | 53 | | 8 | 54 | | 9 | 49 | | 10 | 54 | | 11 | 57 | | 12 | 41 | | 13 | 50 | | 14 | 15 | | 15 | 7 | | 16 | 12 | | 17 | 38 | | 18 | 42 | | 19 | 35 | | 20 | 8 | | 21 | 44 | | 22 | 17 | | 23 | 15 | | 24 | 13 | | 25 | 14 | | 26 | 17 | | 27 | 53 | | 28 | 10 | | 29 | 30 | | 30 | 20 | | 31 | 34 | | 32 | 59 |
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| 93.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 86 | | matches | | 0 | "was known" | | 1 | "was plastered" | | 2 | "were made" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 193 | | matches | | 0 | "was capturing" | | 1 | "were walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 95 | | ratio | 0.095 | | matches | | 0 | "She clocked the scar running along the man's left forearm—Tomás Herrera." | | 1 | "Her worn leather watch ticked again as she checked the time; every second counted." | | 2 | "A few heads turned her way—some curious, some wary." | | 3 | "He spoke furtively to the bartender, then dropped something—a small, bone-like token—into the man’s palm." | | 4 | "The tunnel came alive with the buzz of the Veil Market—merchants hawking enchanted goods, alchemists whispering of forbidden tinctures, and shadows where deals were made with little regard for law." | | 5 | "Finally, he took a wrong turn—an alley that dead-ended in a stone wall." | | 6 | "Her breath caught—a dangerous slip she quickly masked." | | 7 | "But the promise of answers—of finally understanding what happened all those years ago—was a powerful temptation." | | 8 | "Herrera's eyes flickered with something unreadable—a mixture of relief and trepidation." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 259 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.03861003861003861 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.015444015444015444 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 13.21 | | std | 6.59 | | cv | 0.499 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 20 | | 3 | 23 | | 4 | 16 | | 5 | 10 | | 6 | 11 | | 7 | 11 | | 8 | 7 | | 9 | 15 | | 10 | 31 | | 11 | 7 | | 12 | 5 | | 13 | 16 | | 14 | 20 | | 15 | 14 | | 16 | 14 | | 17 | 20 | | 18 | 19 | | 19 | 11 | | 20 | 9 | | 21 | 16 | | 22 | 11 | | 23 | 10 | | 24 | 18 | | 25 | 15 | | 26 | 20 | | 27 | 14 | | 28 | 5 | | 29 | 15 | | 30 | 25 | | 31 | 8 | | 32 | 12 | | 33 | 21 | | 34 | 21 | | 35 | 21 | | 36 | 11 | | 37 | 17 | | 38 | 24 | | 39 | 30 | | 40 | 7 | | 41 | 17 | | 42 | 15 | | 43 | 18 | | 44 | 4 | | 45 | 9 | | 46 | 23 | | 47 | 5 | | 48 | 19 | | 49 | 4 |
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| 61.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.37894736842105264 | | totalSentences | 95 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 86 | | matches | | 0 | "Then she saw him." | | 1 | "Finally, he took a wrong" | | 2 | "Then, with a resigned sigh," |
| | ratio | 0.035 | |
| 71.16% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 86 | | matches | | 0 | "Her sharp jaw was set" | | 1 | "She clocked the scar running" | | 2 | "He was known in certain" | | 3 | "She saw the flash of" | | 4 | "She saw him slip inside." | | 5 | "Her senses, honed by years" | | 6 | "Her worn leather watch ticked" | | 7 | "She returned the nod but" | | 8 | "Her eyes found Herrera at" | | 9 | "His wet clothes clung to" | | 10 | "He spoke furtively to the" | | 11 | "Her partner DS" | | 12 | "She crossed the room, aware" | | 13 | "She heard the faint click," | | 14 | "Her eyes scanned every face," | | 15 | "She could see the tension" | | 16 | "He hadn’t seen her yet." | | 17 | "Her focus was singular." | | 18 | "She moved with military precision," | | 19 | "He twisted, making sharp turns," |
| | ratio | 0.372 | |
| 64.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 86 | | matches | | 0 | "Rain pelted Soho with relentless" | | 1 | "Detective Harlow Quinn’s boots slapped" | | 2 | "Her sharp jaw was set" | | 3 | "The chase had begun in" | | 4 | "The suspect darted sideways, colliding" | | 5 | "Quinn’s conditioned muscles fired, propelling" | | 6 | "She clocked the scar running" | | 7 | "He was known in certain" | | 8 | "Tonight, all that mattered was" | | 9 | "Herrera’s curly dark hair was" | | 10 | "She saw the flash of" | | 11 | "Quinn slowed as she neared" | | 12 | "She saw him slip inside." | | 13 | "The door swung shut, the" | | 14 | "Her senses, honed by years" | | 15 | "Her worn leather watch ticked" | | 16 | "The scent of stale beer" | | 17 | "Maps aged by decades, maybe" | | 18 | "Patrons filled the space, murmuring" | | 19 | "She returned the nod but" |
| | ratio | 0.791 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 94.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 4 | | matches | | 0 | "The suspect darted sideways, colliding with a pedestrian who spun and cursed but barely lost stride." | | 1 | "The scent of stale beer and old wood mingled with a faint smokiness that seemed to cling to the walls." | | 2 | "Maps aged by decades, maybe centuries, covered the walls alongside black-and-white photographs that bore silent witness to countless stories." | | 3 | "Her eyes scanned every face, every stall, searching for the man who was her quarry." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "she called, voice steady" | | 1 | "he said, voice tinged with a blend of defiance and defeat" |
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| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 13 | | tagDensity | 0.231 | | leniency | 0.462 | | rawRatio | 0.333 | | effectiveRatio | 0.154 | |